A conventional power amplifier arrangement having a power amplifier supplied by a static or constant power supply is inefficient and wasteful.
One of the current focuses in power amplification design is improved efficiency. Improvements in efficiency lead to a reduced amplifier cost, for example by allowing the use of less expensive transistors with reduced power handling capability, as well as reduced operating expenses resulting from such factors as reduced size, reduced cooling requirements and reduced power requirements.
One method of obtaining improved efficiency is to modulate a power supply of the power amplifier, for example by using a Vdd modulator. In this way, a power supply signal is modulated as a function of an envelope of an input to the power amplifier arrangement.
The modulated power supply technique has several drawbacks that have an effect on the linearity of a power amplifier. Typically, the Vdd modulator has a finite bandwidth and therefore can not always exactly track the envelope of the input signal. The finite bandwidth of the Vdd modulator has an effect on the gain and phase response of the amplifier.
Effects such as these introduce distortion, which is referred to as “memory”, due to the fact that the effects are a function of past values of the input. The distortion may be addressed when the input to the amplifier is linearized in order to produce an output signal that meets standards requirements for emissions, such as the adjacent channel leakage ratio (ACLR) defined in 3GPP. The majority of conventional linearization techniques assume that the amplifier is memoryless. Therefore, proper linearization of the output signal is not achieved and the amplified signal contains distortion if the amplifier has memory.
Linearization correction techniques that are able to address memory impairments typically suffer from additional limitations. One such limitation is that the techniques begin with the assumption that the distortion is only a function of the input envelope. This is not always true. Another such limitation is that the techniques are very computationally expensive and require large amounts of memory and/or logic resources that typically grow exponentially with the order of a model used in the technique.